Highly Variable Species Distribution Models in a Subarctic Stream
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1 Highly variable species distribution models in a subarctic stream 2 metacommunity: patterns, mechanisms and implications 3 4 Guillermo de Mendoza1,2,*, Riikka Kaivosoja3, Mira Grönroos4, Jan Hjort3, Jari Ilmonen5, 5 Olli-Matti Kärnä3, Lauri Paasivirta6, Laura Tokola3 & Jani Heino7 6 7 1Centre for Advanced Studies of Blanes, Spanish National Research Council (CEAB-CSIC), 8 Accés a la Cala St. Francesc 14, ES-17300 Blanes, Spain. 9 2Laboratoire GEODE, UMR 5602 CNRS, Université Toulouse-Jean Jaurès, 5 allées Antonio 10 Machado, FR-31058 Toulouse, France. 11 3University of Oulu, Geography Research Unit, P.O. Box 3000, FI-90014 Oulu, Finland. 12 4University of Helsinki, Department of Environmental Sciences, Section of Environmental 13 Ecology, Niemenkatu 73, FI-15140 Lahti, Finland. 14 5Metsähallitus, Natural Heritage Services, P.O. Box 94, FI-01301 Vantaa, Finland. 15 6Ruuhikoskenkatu 17, FI-24240 Salo, Finland. 16 7Finnish Environment Institute, Natural Environment Centre, Biodiversity, Paavo Havaksen 17 Tie 3, FI-90570 Oulu, Finland. 18 * Corresponding author: [email protected] 19 20 Summary 21 1. Metacommunity theory focuses on assembly patterns in ecological communities, 22 originally exemplified through four different, yet non-exclusive, perspectives: patch 23 dynamics, species sorting, source-sink dynamics, and neutral theory. More recently, three 24 exclusive components have been proposed to describe a different metacommunity 25 framework: habitat heterogeneity, species equivalence, and dispersal. Here, we aim at 26 evaluating the insect metacommunity of a subarctic stream network under these two 27 different frameworks. 28 2. We first modelled the presence/absence of 47 stream insects in northernmost Finland 29 using binomial generalised linear models (GLMs). The deviance explained by pure local 30 environmental (E), spatial (S), and climatic variables (C) was then analysed across 31 species using beta regression. In this comparative analysis, site occupancy, as well as 32 taxonomic and biological trait vectors obtained from principal coordinate analysis, were 33 used as predictor variables. 34 3. Single-species distributions were better explained by in-stream environmental and spatial 35 factors than by climatic forcing, but in a highly variable fashion. This variability was 36 difficult to relate to the taxonomic relatedness among species or their biological trait 37 similarity. Site occupancy, however, was related to model performance of the binomial 38 GLMs based on spatial effects: as populations are likely to be better connected for 39 common species due to their near ubiquity, spatial factors may also explain better their 40 distributions. 41 4. According to the classical four-perspective framework, the observation of both 42 environmental and spatial effects suggests a role for either mass effects or species sorting 43 constrained by dispersal limitation, or both. Taxonomic and biological traits, including 44 the different dispersal capability of species, were scarcely important, which undermines 45 the patch dynamics perspective, based on differences in dispersal ability between species. 46 The highly variable performance of models makes the reliance on an entirely neutral 47 framework unrealistic as well. According to the three-component framework, our results 48 suggest that the stream insect metacommunity is shaped by the effect of habitat 49 heterogeneity (supporting both species-sorting and mass effects), rather than species 50 equivalence or dispersal limitation. 51 5. While the relative importance of the source-sink dynamics perspective or the species- 52 sorting paradigm cannot be deciphered with the data at our disposal, we can conclude that 53 habitat heterogeneity is an important driver shaping species distributions and insect 54 assemblages in subarctic stream metacommunities. These results exemplify that the use of 55 the three-component metacommunity framework may be more useful than the classical 56 four perspective paradigm in analysing metacommunities. Our findings also provide 57 support for conservation strategies based on the preservation of heterogeneous habitats in 58 a metacommunity context. 59 60 Key-words 61 beta regression, comparative analysis, insects, metacommunity theory, single-species 62 distribution models, stream macroinvertebrates, subarctic streams. 63 64 Introduction 65 Metacommunity theory predicts the assembly of ecological communities according to 66 different perspectives. Originally, this idea was illustrated by Leibold et al. (2004) in the form 67 of four metacommunity perspectives: (1) patch dynamics, which is based on a resource 68 competition-colonisation trade-off among species, thus taking into account species’ dispersal 69 potential (Hanski, 1994); (2) species-sorting along environmental gradients, which relies on 70 differences in environmental tolerance among species (Leibold, 1995); (3) mass effects or 71 source-sink dynamics, whereby species may survive in poor-quality habitats owing to 72 constant immigration from the source populations in high quality habitats (Pulliam 1988); 73 and (4) the neutral theory, where demographic stochasticity solely explains assembly patterns 74 (Hubbell, 2001). Deciphering which of these perspectives is more suitable in the context of 75 metacommunity analysis seems difficult and may well depend on the context of analysis (e.g. 76 spatial extent, biogeographic region, ecosystem type and more; Heino et al., 2015). 77 Nevertheless, the examples of metacommunity perspectives depicted in Leibold et al. 78 (2004) are not mutually exclusive, and represent a fraction of possibilities which can be 79 expanded with the inclusion of species dispersal rates, connectivity, species interactions, 80 disturbance, priority effects, rapid local adaptation, meta-ecosystem dynamics and more 81 (Brown, Sokol, Skelton, & Tornwall, 2017; Logue, Mouquet, Peter, & Hillebrand, 2011). The 82 more recent proposal by Logue et al. (2011) claims that the metacommunity concept is better 83 generalised by three major exclusive components, which decompose the metacommunity 84 framework into (1) environmental heterogeneity, whereby habitat patches differ in 85 environmental attributes; (2) species equivalence, in terms of niche characteristics; and (3) 86 dispersal, referred to as the rate of dispersal among patches. Here, we aim at evaluating 87 species distributions in a subarctic stream insect metacommunity under these two different 88 frameworks (i.e., Leibold et al., 2004 versus Logue et al., 2011), specifically so as to evaluate 89 which of the two is more adequate for the interpretation of our observations. 90 Species distribution models have previously been used to predict community-level 91 properties such as biodiversity (Ferrier & Guisan, 2006). Their accuracy in predicting 92 community-level properties appears to be higher than that of community assembly models, 93 although at a high cost in terms of model complexity (Bonthoux, Baselga, & Balent, 2013, 94 Chapman & Purse, 2011). The accuracy of single-species distribution modelling, however, 95 may also be advantageous to test ecological theories about community assembly mechanisms. 96 This is because accurately modelling the distribution of single species, one at a time, provides 97 the opportunity to proceed with a subsequent comparative analysis across species. Using a 98 comparative analysis, the variation in model performance can be related, for example, to 99 species traits and potential phylogenetic constraints. 100 Stream insect species, in particular, are highly suitable to decipher community 101 assembly processes through the comparative analysis of single-species distribution models 102 (Heino & de Mendoza, 2016). This is because of the high variability among species in 103 tolerance of environmental conditions, as well as resource exploitation, dispersal capability, 104 and habit traits (Merritt & Cummins, 1996; Tachet, Richoux, Bournaud, & Usseglio-Polatera, 105 2010; Schmidt-Kloiber & Hering, 2015; Serra, Cobo, Graça, Dolédec, & Feio, 2016). This 106 variability is valuable in evaluating which community assembly mechanism dominates in 107 each particular context of analysis. Basically, such an analysis might shed light into the 108 relevance of environmental variables, spatial variables, and dispersal capability of species on 109 model performance. Subsequently, this information can be used as an indicator of the 110 preponderance of one community assembly mechanism over another (Figure 1). For example, 111 if many species show similar spatial patterns, and if these species share the same dispersal 112 potential, we can presume that the ability to disperse may be underlying the observed general 113 pattern for these species. This would give us hints about the adequacy to consider one 114 particular metacommunity theory perspective over the others. Within the classical four- 115 perspective framework (Leibold et al., 2004), patch dynamics would likely be suitable in this 116 case, as this perspective relies on the different capability of species to both disperse and 117 exploit resources. Within the metacommunity framework based on three exclusive 118 components (Logue et al., 2011), dispersal would be main driver in this case. Moreover, 119 stream insects are also a diverse group of species, which belong to different insect orders and 120 vary widely in physiological and morphological adaptations (Merritt & Cummins, 1996). 121 Thus, modelling the distribution